1 Department of Clinical Neurosciences, University of Calgary, Calgary, Canada.
2 Department of Mechanical Engineering, University of Victoria, Victoria, Canada.
3 Division of Medical Sciences, University of Victoria, Victoria, Canada.
4 Multiomics Investigation of Neurodegenerative Diseases (MIND) Laboratory, Montréal, Canada.
5 Département de pharmacologie et physiologie, Faculté de médecine, Université de Montréal, Montréal, Canada.
6 Institut de génie biomédical, Université de Montréal, Montréal, Canada.
7 Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal (CRIUGM), Montréal, Canada.
8 Department of Radiology, University of Calgary, Calgary, Canada.
9 Hotchkiss Brain Institute, University of Calgary, Calgary, Canada.
10 Department of Clinical Neurosciences, Hôpital de l'Enfant-Jésus, CHU de Québec, Université Laval, Québec, QC, Canada.
11 Centre de Recherche du CHU de Québec, Centre hospitalier de l'Université Laval (CHUL), Université Laval, Québec, QC, Canada.
12 Aging, Mobility, and Cognitive Health Laboratory, University of British Columbia, Vancouver, BC, Canada.
13 Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada.
14 Centre for Aging SMART at Vancouver Coastal Health, Vancouver Coastal Health Research Institute, Vancouver, BC, Canada.
15 Department of Physical Therapy, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada.
16 Department of Physics, Concordia University, Montréal, Québec, Canada.
17 School of Health, Concordia University, Montréal, Québec, Canada.
18 Centre ÉPIC, Montreal Heart Institute, Montréal, Québec, Canada.
19 Vulnerable Brain Lab, Department of Anatomy and Cell Biology, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.
20 Douglas Research Centre, Montreal, Quebec, Canada.
21 Department of Psychiatry, McGill University, Montreal, Quebec, Canada.
22 Integrated Program in Neuroscience, McGill University, Montreal, Quebec, Canada.

Cerebral small vessel disease (CSVD) can manifest as brain lesions visible on magnetic resonance imaging, including white matter hyperintensities (WMH), cerebral microbleeds (CMB), perivascular spaces (PVS), lacunes, and recent small subcortical infarcts (RSSI). Detection and segmentation of these imaging markers can provide valuable information on brain health, including prevention and treatment of dementia. However, manual segmentation is cumbersome, especially for large cohorts in research studies. There has been extensive research into the development of automated tools using machine learning to increase accuracy and efficiency in lesion segmentation. This systematic review aimed to summarize novel automated methods developed over the last 10 years that segment CSVD lesion types and have been validated on a population with or at risk for CSVD (e.g., older adults, those with cognitive disorders, or those with vascular risk factors). A search on Web of Science and PubMed yielded 2764 studies, of which 89 were included after screening and full text review. 59 of these methods segmented WMH, 23 detected or classified CMB, 6 detected or segmented PVS, 5 detected, classified, or segmented lacunes, and 2 segmented RSSI. Of these, 30 studies (23 for WMH, 5 for CMB, 1 for PVS, and 1 for lacunes) included links to download code or pre-trained models, including one commercial tool, and one that relied on a commercial tool for input. Overall, this review found good evidence for high quality tools available for WMH segmentation, with fewer tools available to accurately segment other CSVD lesion types.